Citation: Moffett A, Strutz S, Guda N, González C, Ferro MC, Sánchez-Cordero V, et al. (2009) A Global Public Database of Disease Vector and Reservoir Distributions. PLoS Negl Trop Dis 3(3): e378. https://doi.org/10.1371/journal.pntd.0000378
Editor: Juerg Utzinger, Swiss Tropical Institute, Switzerland
Published: March 31, 2009
Copyright: © 2009 Moffett et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: VS-C thanks the Universidad Nacional Autónoma de México (Project PAPIIT 225408) for financial support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
The Disease Vector Database is a global, free, public, Web-accessible resource presenting data on the geographical distribution of infectious disease vectors and reservoirs. At present, the Database contains records for dengue and malaria vectors and Chagas disease and leishmaniasis vectors and reservoirs. Future versions of the Database will include parasite data. These data can be used for a variety of purposes including the construction of ecological niche models and disease risk maps. The Database permits downloading data in formats designed to facilitate such use, for instance, as input files for popular niche modeling software packages such as Maxent and GARP.
Vector-borne infectious diseases adversely affect the health of large numbers of people globally. Effective response to these diseases requires representations of risk, which often take the form of risk maps –, with geographic estimates of risk based on distributions of parasite, vector, and reservoir species in addition to human population, social organization, and behavior. The feasibility of constructing risk maps has increased substantially in recent years due to the widespread availability of digital environmental layers, geographic information system (GIS) platforms, and advances in ecological niche modeling, which makes it possible to produce maps even with very few parasite, reservoir, and vector presence records . Niche models have been used to predict the geographic distributions of Chagas disease and leishmaniasis vectors and reservoirs and dengue and malaria vectors 3–5.
The construction of risk maps and other forms of spatial risk assessment require the availability of georeferenced parasite, vector, and reservoir records. Several groups have suggested the creation of public repositories of such spatial data ,. We have created the Disease Vector Database (http://www.diseasevectors.org), a global, free, publicly accessible Web-based database for disease vector and reservoir data. Future versions of the Database will include parasite data. At present, the Database has occurrence records for vector and reservoir species for Chagas disease and leishmaniasis, and vector species for dengue and malaria (Table 1). Researchers from around the world may submit data for inclusion in the Database following a submission protocol similar to that of GenBank .
There are some existing databases for disease vectors, mainly for malaria. However, compared to the effort being reported here, each of these databases has some limitations (though some provide useful additional data beyond the scope of the Database described here). In 1996, the Mapping Malaria Risk in Africa (MARA) collaboration (http://www.mara.org.za/) began to provide a repository of information on malaria-transmission intensities in Africa . Though the focus of the collaboration was on parasite data rather than on vector data, it currently contains 2,535 georeferenced records of malaria vectors. However, the records cover only the six members of the Anopheles gambiae complex, and there is no information from continents other than Africa. Koum and colleagues (2005)  created a database of malaria vectors with georeferenced information sampled from 19 African locations. However, the focus was on establishing the proper ontology for such a database and not on public use. The Walter Reed Army Institute and the Smithsonian Institution's MosquitoMap Web site (http://www.mosquitomap.org/) contains distributional information on mosquito species.
Chagas disease, dengue, leishmaniasis, and malaria were selected for initial inclusion in our Database because of the epidemiologic importance of vector and reservoir control for the prevention of their transmission and the availability of data. For the Database, the vectors and reservoirs implicated in the four diseases were identified from reviews. There were three sources for the data. First, a number of the data points (for instance, ∼10% of the reservoir data and ∼75% of the leishmaniasis vector data) came from field records of collaborators collected over several decades. This will eventually be the most important source of new data. Second, the ISI Web of Knowledge and Google Scholar were searched using “distribution” along with the genus and name of each species. References from the publications identified on the basis of this search were also consulted for additional data. Third, records from public databases such as MANIS (for mammal reservoir data; http://manisnet.org/), MARA, and MosquitoMap were included.
A record is included in the Database only if it meets the following criteria: (a) it represents a confirmed or suspected disease vector or reservoir; (b) it includes the species of the vector or reservoir; (c) it can be georeferenced to at least the nearest arc-minute. When available, additional information is included for the following: abundance, collection method, collector contact information, country, date, location, provenance, region, and voucher location. Each record has a unique accession number; information is maintained on the dates of initial inclusion and last modification. Data on the origin of the information are also retained. Records referenced only by names of study sites or by points on published maps were included only if they could be adequately georeferenced. The spatial data associated with these records may be less accurate than those for records with published coordinates: The Database records such uncertainties so that users can decide whether records are sufficiently precise for their needs. Table 2 discusses advantages and disadvantages of the Database.
The Web site enables the submission of data from an unlimited number of collaborators, who are then listed on the Web page. Data from the Disease Vector Database can be used in a variety of ways to help us understand the geographic risk of disease. Restricting attention to publicly available data, maps of occurrence records from the Database can be used to identify areas that need attention. For instance, the neotropics south of México in Central America have not been adequately sampled for Chagas disease vectors and reservoirs (see Figure 1). Data on potential leishmaniasis reservoirs are lacking from areas outside North America; data on vectors are incomplete except in Latin America. As additional existing data are incorporated into the Database, the geographic limitations will become less severe. Nevertheless, it is likely that there will remain significant areas with no data because they have not been surveyed. Compared to malaria and dengue, strikingly little data are publicly available for Chagas disease and leishmaniasis.
Yellow, malaria vectors; green, dengue vectors; blue, Chagas reservoirs and vectors; red, leishmaniasis reservoirs and vectors.
The other most obvious use of such data is for ecological niche modeling. For malaria, this Database has already been used to predict continent-wide distribution maps for ten malaria vector species in Africa, with these maps then being used to produce relative risk maps . The long-term goal of the Disease Vector Database is to include many more diseases. Success of the project will depend on active collaboration of researchers from around the world. The fact that this Database could be constructed already shows the extent to which free datasharing is becoming the norm rather than the exception in epidemiology and ecology. Success in extending the Database will depend on a continuation of this trend, and this note is intended to encourage further collaboration.
- 1. Hay SI, Snow RW (2006) The Malaria Atlas Project: Developing global maps of malaria risk. PLoS Med 3: e473.SI HayRW Snow2006The Malaria Atlas Project: Developing global maps of malaria risk.PLoS Med3e473
- 2. Brooker S, Utzinger J (2007) Integrated disease mapping in a polyparasitic world. Geospat Health 1: 141–146.S. BrookerJ. Utzinger2007Integrated disease mapping in a polyparasitic world.Geospat Health1141146
- 3. Peterson AT (2006) Ecological niche modeling and spatial patterns of disease transmission. Emerg Infect Dis 12: 1822–1826.AT Peterson2006Ecological niche modeling and spatial patterns of disease transmission.Emerg Infect Dis1218221826
- 4. Moffett A, Shackelford N, Sarkar S (2007) Malaria in Africa: Vector species' niche models and relative risk maps. PLoS ONE 2: e824.A. MoffettN. ShackelfordS. Sarkar2007Malaria in Africa: Vector species' niche models and relative risk maps.PLoS ONE2e824
- 5. Peterson AT, Martinez-Campos C, Nakazawa Y, Martinez-Meyer E (2005) Time-specific ecological niche modeling predicts the spatial dynamics of vector insects and human dengue cases. Trans R Soc Trop Med Hyg 99: 647–655.AT PetersonC. Martinez-CamposY. NakazawaE. Martinez-Meyer2005Time-specific ecological niche modeling predicts the spatial dynamics of vector insects and human dengue cases.Trans R Soc Trop Med Hyg99647655
- 6. Eisen L, Eisen RJ (2007) Need for improved methods to collect and present spatial epidemiologic data for vectorborne diseases. Emerg Infect Dis 13: 1816–1820.L. EisenRJ Eisen2007Need for improved methods to collect and present spatial epidemiologic data for vectorborne diseases.Emerg Infect Dis1318161820
- 7. Foley DH, Weitzman AL, Miller SE, Farna ME, Rueda LM, et al. (2008) The value of georeferenced collection records for predicting patterns of mosquito species richness and endemism in the Neotropics. Eco Ent 33: 12–23.DH FoleyAL WeitzmanSE MillerME FarnaLM Rueda2008The value of georeferenced collection records for predicting patterns of mosquito species richness and endemism in the Neotropics.Eco Ent331223
- 8. Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL (2006) Genbank. Nucleic Acids Res 34: D16–D20.DA BensonI. Karsch-MizrachiDJ LipmanJ. OstellDL Wheeler2006Genbank.Nucleic Acids Res34D16D20
- 9. Le Sueur D, Binka F, Lengeler C, de Savigny D, Snow B, et al. (1997) An atlas of malaria in Africa. Afr Health 19: 23–24.D. Le SueurF. BinkaC. LengelerD. de SavignyB. Snow1997An atlas of malaria in Africa.Afr Health192324
- 10. Koum G, Yekel A, Ndifon B, Simard F (2005) Design and implementation of a mosquito database through an entomological ontology. Bioinform 21: 2797–2802.G. KoumA. YekelB. NdifonF. Simard2005Design and implementation of a mosquito database through an entomological ontology.Bioinform2127972802